E-commerce Analytics Course

Lesson 1 • Analytics Maturity and Goal Setting

Introduces the analytics maturity model and how businesses progress from descriptive to predictive insight. Guides students in setting measurable analytics objectives.

Lesson 2 • Data Collection Methods and Tools

Surveys the primary methods for capturing e-commerce data, including pixel tracking, server logs, and APIs. Prepares students to evaluate tool fit for their context.

Lesson 3 • Core E-Commerce KPIs

Defines the essential metrics that measure store health and growth. Connects each KPI to a specific business decision.

Lesson 1 • Implementing E-Commerce Tracking

Covers step-by-step implementation of standard and enhanced e-commerce tracking. Directly enables the KPI measurement introduced in Chapter 1.

Lesson 2 • Custom Dimensions and Metrics

Extends default analytics data with business-specific attributes such as membership tier or product category. Enables richer segmentation in later chapters.

Lesson 3 • Data Quality and Validation

Teaches systematic methods to detect and fix tracking errors before they corrupt reports. Ensures the data foundation is reliable for all subsequent analysis.

Lesson 4 • Tag Management for E-Commerce

Demonstrates using a tag management system to deploy and maintain tracking tags without code changes. Reduces implementation errors and speeds iteration.

Lesson 1 • Segmentation for Behavioural Insights

Applies audience segments to isolate behavioural differences between customer groups. Enables targeted recommendations grounded in data.

Lesson 2 • On-Site Behaviour Metrics

Examines engagement signals such as bounce rate, session duration, and page depth. Reveals how content and UX influence purchase intent.

Lesson 3 • Cohort and Path Analysis

Groups users by acquisition date or behaviour to track retention and repeat purchase patterns over time. Extends funnel analysis into longitudinal customer journeys.

Lesson 4 • Traffic Source Analysis

Breaks down how visitors arrive via organic, paid, social, and direct channels. Connects acquisition source to downstream conversion behaviour.

Lesson 1 • Personalisation as a CRO Strategy

Introduces data-driven personalisation as an advanced extension of A/B testing. Shows how behavioural segments from Chapter 3 power targeted experiences.

Lesson 2 • Multivariate and Split URL Testing

Extends A/B testing to multiple simultaneous variables and separate page versions. Enables more complex optimisation scenarios on high-traffic pages.

Lesson 3 • A/B Testing Fundamentals

Covers the statistical principles behind controlled experiments and how to design valid tests. Directly applies funnel and behavioural data from Chapter 3.

Lesson 4 • CRO Frameworks and Prioritisation

Introduces structured frameworks for generating and ranking optimisation hypotheses. Prevents wasted effort by focusing tests on high-impact opportunities.

Lesson 1 • Customer Acquisition Cost Optimisation

Applies attribution insights to reduce CAC while maintaining acquisition volume. Directly builds on the CAC metric introduced in Chapter 1.

Lesson 2 • Cross-Channel Campaign Analysis

Integrates data from multiple ad platforms into a unified performance view. Reveals channel interaction effects invisible in single-platform reports.

Lesson 3 • Multi-Touch Attribution Models

Compares first-touch, last-touch, linear, and data-driven attribution models. Enables accurate credit assignment across the customer journey.

Lesson 4 • Organic and Content Channel Analytics

Measures SEO, email, and content marketing performance using analytics data. Balances paid channel focus with lower-cost acquisition strategies.

Lesson 1 • Loyalty Programme Analytics

Measures the incremental impact of loyalty programmes on purchase frequency and CLV. Distinguishes genuine loyalty lift from selection bias in programme data.

Lesson 2 • Retention Dashboard Design

Translates CLV and retention metrics into an actionable monitoring dashboard. Prepares students to communicate retention health to stakeholders.

Lesson 3 • RFM Segmentation for Retention

Applies recency, frequency, and monetary scoring to segment customers by loyalty and risk. Enables targeted retention campaigns grounded in purchase behaviour.

Lesson 4 • Churn Prediction and Prevention

Builds early-warning indicators for customer churn using behavioural and transactional signals. Connects churn reduction directly to CLV improvement.

Lesson 1 • Merchandising and Assortment Analytics

Uses data to evaluate product mix, cross-sell opportunities, and catalogue gaps. Directly informs assortment and merchandising decisions.

Lesson 2 • Product Performance Reporting

Analyses revenue, units sold, and margin by product and category. Builds on enhanced e-commerce tracking configured in Chapter 2.

Lesson 3 • Search and Discovery Analytics

Analyses on-site search queries and browse behaviour to surface product discovery gaps. Feeds directly into merchandising and CRO priorities.

Lesson 4 • Inventory and Demand Forecasting

Applies sales trend data to forecast demand and reduce stockout and overstock costs. Connects product analytics to supply chain decisions.

Lesson 1 • Analytics-Driven Business Cases

Structures data findings into persuasive business cases that justify investment decisions. Applies the full analytical toolkit to real strategic scenarios.

Lesson 2 • Executive Dashboard Development

Designs a comprehensive executive dashboard integrating KPIs from acquisition, retention, and product chapters. Translates operational data into strategic narratives.

Lesson 3 • Data Visualisation Best Practices

Teaches principles of effective chart selection, layout, and storytelling for analytics reports. Ensures insights from all prior chapters are communicated clearly.

Lesson 4 • Building an Analytics Roadmap

Guides students in creating a phased analytics improvement plan for their organisation. Synthesises all course learning into a strategic, actionable deliverable.